Include domain experts in data and model reviews.<\/li>\n<\/ul>\n\n\n\nAdding more data does not automatically solve bias. The new data must address the missing populations, situations, and failure cases.<\/p>\n\n\n\n
Common Data-Splitting Mistakes in Machine Learning Projects<\/h2>\n\n\n\n1. Preprocessing Before Splitting<\/h3>\n\n\n\n
Scaling, imputing, feature selection, or encoding the complete dataset before splitting allows information from the validation or test set to affect training.<\/p>\n\n\n\n
Split first and fit preprocessing steps only on the training data. A pipeline helps apply the same fitted transformations safely.<\/p>\n\n\n\n
2. Using the Test Set for Model Tuning<\/h3>\n\n\n\n
Checking test performance after every model change turns the test set into an unofficial validation set.<\/p>\n\n\n\n
Keep it hidden until the model, features, thresholds, and hyperparameters are finalized.<\/p>\n\n\n\n
3. Randomly Splitting Time-Series Data<\/h3>\n\n\n\n
A random split may place future observations in the training set and older observations in the test set.<\/p>\n\n\n\n
This creates future-data leakage. Time-based problems should preserve chronological order. Scikit-learn provides TimeSeriesSplit<\/code> for time-ordered data where standard cross-validation would train on future observations.<\/p>\n\n\n\n4. Ignoring Class Imbalance<\/h3>\n\n\n\n
A random split may place too few minority-class examples in one subset.<\/p>\n\n\n\n
Use stratification for classification problems when appropriate:<\/p>\n\n\n\n
X_train, X_test, y_train, y_test = train_test_split(\n X,\n y,\n test_size=0.20,\n stratify=y,\n random_state=42\n)<\/code><\/pre>\n\n\n\n5. Splitting Related Records Across Sets<\/h3>\n\n\n\n
Records belonging to the same person, customer, patient, device, or household should not appear across both training and test sets.<\/p>\n\n\n\n
The model may memorize entity-specific patterns rather than learn general relationships. Group-based splitting is more suitable in such cases.<\/p>\n\n\n\n
6. Leaving Duplicate Records Across Splits<\/h3>\n\n\n\n
Duplicate or nearly identical records can create inflated evaluation scores. Duplicates should be identified before splitting, or all versions of the same example should remain within one subset.<\/p>\n\n\n\n
7. Using a Test Set That Is Too Small<\/h3>\n\n\n\n
A tiny test set may produce unstable metrics. A few correct or incorrect predictions can change the reported result substantially. The test set should contain enough examples to represent important classes and operating conditions.<\/p>\n\n\n\n
8. Choosing the Split Ratio Mechanically<\/h3>\n\n\n\n
An 80\/20 split is common, but it is not always correct. A dataset containing millions of records may need only a small test percentage. A small dataset may benefit from cross-validation rather than a large permanent validation set.<\/p>\n\n\n\n
9. Ignoring Data Distribution Changes<\/h3>\n\n\n\n
Training and test data drawn from the same historical period may not represent future users or conditions. Teams should examine seasonality, geography, device types, policy changes, and other sources of distribution shift.<\/p>\n\n\n\n
10. Applying Data Augmentation Before Splitting<\/h3>\n\n\n\n
Creating transformed copies before the split may place the original record in training and a slightly modified copy in testing. Split the original records first. Apply augmentation only to the training subset.<\/p>\n\n\n\n
Conclusion<\/h2>\n\n\n\n
Training, validation, and test data each support a different stage of machine learning development. Training data helps the model learn patterns, validation data helps refine its settings, and test data measures its final performance on unseen examples.<\/p>\n\n\n\n
A correct data split prevents leakage, reduces overfitting, and produces more reliable evaluation results. The split ratio should match the dataset size, class balance, time order, and project requirements rather than follow one fixed formula.<\/p>\n\n\n\n
Clear separation between all three datasets helps teams build models that perform well during development and remain dependable in real-world use.<\/p>\n\n\n\n
If you\u2019re eager to strengthen your foundation in data science and learn practical skills like data preparation, visualization, and model building, consider joining the Data Science Course<\/strong><\/a>. This program offers mentor-led training, hands-on projects, and job-ready modules covering Python, Machine Learning, and Data Visualization, helping you become confident in applying these concepts in real-world scenarios.<\/p>\n\n\n\nFAQs<\/h2>\n\n\n\n
\n
\n
1. Can I use the same dataset for training and testing?<\/strong><\/h3>\n\n\n
No, because it will make the model memorize the data, leading to poor real-world performance.<\/p>\n\n<\/div>\n<\/div>\n
\n
2. What if my dataset is too small to split?<\/strong><\/h3>\n\n\n
In that case, you can use cross-validation<\/strong>, which splits data into multiple smaller sets for better evaluation.<\/p>\n\n<\/div>\n<\/div>\n\n
3. How can I know if my model is overfitting?<\/strong><\/h3>\n\n\n
If your model performs very well on training data but poorly on testing data, it\u2019s likely overfitting.<\/p>\n\n<\/div>\n<\/div>\n
\n
4. Can I use a different split ratio than 80\/20?<\/strong><\/h3>\n\n\n
Yes, depending on your dataset size. For large datasets, even 90\/10 can work well.<\/p>\n\n<\/div>\n<\/div>\n
\n
5. Should I include outliers in training and testing data?<\/strong><\/h3>\n\n\n
You can include them, but always analyze their impact \u2014 sometimes they improve learning, other times they distort results.<\/p>\n\n<\/div>\n<\/div>\n<\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"
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